Each timeseries is identified by the conjunction of an `reference` and a `symbol`.It is structured this way so that the source (or type) of the data can be declared as the `reference`.Example:- While in the stockmarket context, the `reference` can be NASDAQ while `symbol` is left for the company stock.- Storing country 'UN Human Development Index' the `reference` can be `HDI` while the `symbol` would take a country's name or code.

Here you can find:

- A __Storage__ implementation that offers methods to save / update, retrieve and delete `pandas dataframes`- A __flask extension__ that exposes an REST API that handles data as json- A __REST client__ that can communicate with the REST API- A __command line script__ that enables shell usage of the REST API- Some __bigtempo datasources__ that allows easy integration, after all, `store api` was conceived exactly to serve data to `bigtempo`.

### Storage implementationFor the moment the is only one implementation based on SQLAlchemy.You can find it at `flask_bigtempo/store/storages.py`.Example usage can be found `flask_bigtempo/store/clients.py`